Evaluators’ Consideration of Warmth and Competence in Verbal and Numerical Performance Assessments

When conducting performance appraisals, evaluators largely depend on their subjective perception. This makes performance appraisals particularly vulnerable to biases, especially along the lines of warmth and competence – the two primary dimensions of social judgment. Warmth reflects the ability to build and maintain social relationships, while competence refers to achieving goals and completing tasks. Although both dimensions have been extensively studied in the past, there is limited understanding of how they influence observation-based assessments, particularly in relation to the evaluation format and the impact of the rater and ratee gender. To address this gap, the study employs a laboratory experiment to investigate how warmth- and competence-related behaviors in a task setting translate into performance appraisals, namely numerical ratings, written comments, and spoken comments. The evaluation comments are converted into numerical ratings using a machine learning algorithm, which allows for comparison with the assigned numerical ratings. Findings reveal that the consideration of warmth and competence depends not only on the appraisal format but also on the rater (evaluator) and ratee (task-solver) gender. This study enhances the understanding of how evaluations differ across formats and examines the role of gender in shaping perceptions of warmth and competence.